Abstract
An agent-based modelling approach is a powerful means of understanding social phenomena by modelling individual behaviours and interactions. However, the advancements in modelling pose challenges in the model analysis process for understanding the complex effects of input factors, especially when it comes to offering concrete policies for improving system outcomes. In this work, we propose a revised micro-dynamic analysis method that adopts advanced artificial intelligence methods to enhance the model interpretation and to facilitate group-specific policy-making. It strengthens the explanation power of the conventional micro-dynamic analysis by eliminating ambiguity in the result interpretation and enabling a causal interpretation of a target phenomenon across subgroups. We applied our method to understand an agent-based model that evaluates the effects of a long-term care scheme on access to care. Our findings showed that the method can suggest policies for improving the equity of access more efficiently than the conventional scenario analysis.
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References
Andersen, R.M.: Revisiting the behavioral model and access to medical care: Does it matter? J. Health Soc. Behav. 36(1), 1–10 (1995). http://www.jstor.org/stable/2137284
Atun, R.: Health systems, systems thinking and innovation. Health Policy Plan. 27(SUPPL. 4), 4–8 (2012)
Auchincloss, A.H., Garcia, L.M.: Brief introductory guide to agent-based modeling and an illustration from urban health research. Cad. Saude Publica 31(1), 65–78 (2015). https://doi.org/10.1590/0102-311X00051615
Bonabeau, E.: Agent-based modeling: Methods and techniques for simulating human systems. Proc. Natl. Acad. Sci. 99(suppl 3), 7280–7287 (2002). https://doi.org/10.1073/pnas.082080899
Braithwaite, J.: Growing inequality: bridging complex systems, population health and health disparities. Int. J. Epidemiol. 351–353 (2018). https://doi.org/10.1093/ije/dyy001, http://academic.oup.com/ije/advance-article/doi/10.1093/ije/dyy001/4819238
Chang, S., Yang, W., Deguchi, H.: Care providers, access to care, and the long-term care nursing insurance in china: An agent-based simulation. Soc. Sci. Medicine 244, 112667 (2020). https://doi.org/10.1016/j.socscimed.2019.112667
Dong, G., Li, J.: Efficient mining of emerging patterns: Discovering trends and differences. In: Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 1999, pp. 43–52. Association for Computing Machinery, New York (1999). https://doi.org/10.1145/312129.312191
Edali, M., Yücel, G.: Exploring the behavior space of agent-based simulation models using random forest metamodels and sequential sampling. Simulation Model. Practice Theory 92, 62–81 (2019). https://doi.org/10.1016/j.simpat.2018.12.006, https://www.sciencedirect.com/science/article/pii/S1569190X18301941
Feng, Z., Liu, C., Guan, X., Mor, V.: China’s rapidly aging population creates policy challenges in shaping a viable long-term care system. Health Affairs 31, 2764–73 (2012). https://doi.org/10.1377/hlthaff.2012.0535
Gilbert, G.N.: Agent-based models. Quantitative applications in the social sciences. Sage (2008)
Glymour, C., Zhang, K., Spirtes, P.: Review of causal discovery methods based on graphical models. Front. Genet. 10, 524 (2019). https://doi.org/10.3389/fgene.2019.00524
Hamill, L.: Agent-based modelling: The next 15 years. J. Artif. Societies Soc. Simul. 13(4), 7 (2010). https://doi.org/10.18564/jasss.1640, https://www.jasss.org/13/4/7.html
Iwashita, H., Takagi, T., Suzuki, H., Goto, K., Ohori, K., Arimura, H.: Efficient constrained pattern mining using dynamic item ordering for explainable classification. CoRR abs/ arXiv: 2004.08015 (2020)
Kotoku, J., et al.: Causal relations of health indices inferred statistically using the DirectLiNGAM algorithm from big data of Osaka prefecture health checkups. PLoS ONE 15(12), e0243229 (2020). https://doi.org/10.1371/journal.pone.0243229
Langellier, B.A.: An agent-based simulation of persistent inequalities in health behavior: Understanding the interdependent roles of segregation, clustering, and social influence. SSM - Popul. Health 2, 757–769 (2016). https://doi.org/10.1016/j.ssmph.2016.10.006, https://www.sciencedirect.com/science/article/pii/S2352827316301112
Lee, J.S., et al.: The complexities of agent-based modeling output analysis. J. Artifi. Societies Soc. Simul. 18(4), 4 (2015). https://doi.org/10.18564/jasss.2897, http://jasss.soc.surrey.ac.uk/18/4/4.html
Malleson, N., Heppenstall, A., See, L., Evans, A.: Using an agent-based crime simulation to predict the effects of urban regeneration on individual household burglary risk. Environ. Planning B: Planning and Design 40(3), 405–426 (2013). https://doi.org/10.1068/b38057
Ogarrio, J.M., Spirtes, P., Ramsey, J.: A hybrid causal search algorithm for latent variable models. In: Antonucci, A., Corani, G., Campos, C.P. (eds.) Proceedings of the Eighth International Conference on Probabilistic Graphical Models. Proceedings of Machine Learning Research, vol. 52, pp. 368–379. PMLR, Lugano, Switzerland (2016)
Ohori, K., Takahashi, S.: Market design for standardization problems with agent-based social simulation. J. Evol. Econ. 22(1), 49–77 (2012). https://doi.org/10.1007/s00191-010-0196-y
Pereda, M., Santos, J.I., Galán, J.M.: A brief introduction to the use of machine learning techniques in the analysis of agent-based models. In: Hernández, C. (ed.) Advances in Management Engineering. LNMIE, pp. 179–186. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-55889-9_11
Qingdao Municipal Bureau of Human Resource and Social Secure: A list of LTC service providers of qingdao six districts (2016). http://www.qdhrss.gov.cn/pages/hdjl/fwdh/59947.html/
Ramsey, J., Glymour, M., sanchez romero, R., Glymour, C.: A million variables and more: the fast greedy equivalence search algorithm for learning high-dimensional graphical causal models, with an application to functional magnetic resonance images. Int. J. Data Sci. Anal. 3, 121–129 (2017). https://doi.org/10.1007/s41060-016-0032-z
Shen, X., Ma, S., Vemuri, P., Simon, G.: Alzheimer’s Disease Neuroimaging Initiative: Challenges and opportunities with causal discovery algorithms: Application to alzheimer’s pathophysiology. Sci. Rep. 10(1), 2975 (2020). https://doi.org/10.1038/s41598-020-59669-x
Shimizu, S., et al.: Directlingam: A direct method for learning a linear non-gaussian structural equation model. J. Mach. Learn. Res. 12(null), 1225–1248 (2011)
Spirtes, P., Glymour, C., Scheines, R. (eds.): Causation, Prediction, and Search. LNS, vol. 81. Springer, New York (1993). https://doi.org/10.1007/978-1-4612-2748-9
Spirtes, P., Glymour, C., Scheines, R., Kauffman, S., Aimale, V., Wimberly, F.: Constructing bayesian network models of gene expression networks from microarray data. In: Proceedings of the Atlantic Symposium on Computational Biology (2000). https://doi.org/10.1184/R1/6491291.v1
Yamada, H., Yamane, S., Ohori, K., Kato, T., Takahashi, S.: A method for micro-dynamics analysis based on causal structure of agent-based simulation. In: Bae, K.H., Feng, B., Kim, S., Lazarova-Molnar, S., Zheng, Z., Roeder, T., Thiesing, R. (eds.) 2020 Winter Simulation Conference (WSC), pp. 313–324. IEEE Press, Piscataway, New Jersey (2020)
Yamane, S., et al.: Systematic analysis of micro dynamics in agent based simulation. In: Rabe, M., Juan, A., Mustafee, N., Skoogh, A., Jain, S., Johansson, B. (eds.) 2018 Winter Simulation Conference (WSC), pp. 4214–4215. IEEE Press, Piscataway, New Jersey (2018)
Yang, C., Kurahashi, S., Kurahashi, K., Ono, I., Terano, T.: Agent-based simulation on women’s role in a family line on civil service examination in chinese history. J. Artif. Societies Soc. Simul. 12(2), 5 (2009). https://www.jasss.org/12/2/5.html
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This work was partially supported by JSPS KAKENHI Grant Number 20K18958.
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Chang, S., Asai, T., Koyanagi, Y., Uemura, K., Maruhashi, K., Ohori, K. (2023). Incorporating AI Methods in Micro-dynamic Analysis to Support Group-Specific Policy-Making. In: Aydoğan, R., Criado, N., Lang, J., Sanchez-Anguix, V., Serramia, M. (eds) PRIMA 2022: Principles and Practice of Multi-Agent Systems. PRIMA 2022. Lecture Notes in Computer Science(), vol 13753. Springer, Cham. https://doi.org/10.1007/978-3-031-21203-1_8
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